A user guide to simple monitoring and sustainable operation of PV-diesel hybrid systems : Handbook for system users and operators

This report contains a suggestion for a simple monitoring and evaluation guideline for PV-diesel hybrid systems. It offers system users a way to better understand if their system is operated in a way that will make it last for a long time. It also gives suggestions on how to act if there are signs of unfavourable use or failure. The application of the guide requires little technical equipment, but daily manual measurements. For the most part, it can be managed by pen and paper, by people with no earlier experience of power systems.The guide is structured and expressed in a way that targets PV-diesel hybrid system users with no, or limited, earlier experience of power engineering. It is less detailed in terms of motivations for certain choices and limitations, but rich in details concerning calculations, evaluation procedures and maintenance routines. A more scientific description of the guide can be found in a related journal article.

Rapid Assessment of Seed Removal and Frugivore Activity as a Tool for Monitoring the Health Status of Tropical Forests

Ecological processes in tropical forests are being affected at unprecedented rates by human activities. Yet, the continuity of ecological functions like seed dispersal is crucial for forest regeneration. It thus becomes increasingly urgent to be able to rapidly assess the health status of these processes in order to take appropriate management measures. We tested a method to rapidly evaluate seed removal rates on two animal-dispersed tree species, Virola kwatae and V.michelii (Myristicaceae), at three sites in French Guiana with increasing levels of anthropogenic disturbance. We counted fallen fruits, fruit valves, and seeds of each focal fruiting tree in a single 1m2 quadrat, and calculated two indices: the proportion of seeds removed and the proportion of fruits opened by mammals. They both provide an indirect and rapid assessment of frugivore activity. Our results showed a significant decrease in the proportion of removed seeds (16%) and fruits opened (19%) at the most impacted site in comparison with the other two sites (79% for seeds, 60% and 35% for fruits). This testifies to an increased impoverishment of the primate and toucan communities at the disturbed sites. This standardized protocol provides fast information about the health status of the community of seed dispersers and predators and of their seed removal services. It is time- and cost-effective and is not species-specific, allowing comparisons among sites or over time. We suggest using it with the pantropical Myristicaceae and any other capsule-producing family to rapidly assess the health status of seed removal processes across the tropics.

Mental states monitoring through passive Brain-Computer Interface systems

The monitoring of cognitive functions aims at gaining information about the current cognitive state of the user by decoding brain signals. In recent years, this approach allowed to acquire valuable information about the cognitive aspects regarding the interaction of humans with external world. From this consideration, researchers started to consider passive application of brain–computer interface (BCI) in order to provide a novel input modality for technical systems solely based on brain activity. The objective of this thesis is to demonstrate how the passive Brain Computer Interfaces (BCIs) applications can be used to assess the mental states of the users, in order to improve the human machine interaction. Two main studies has been proposed. The first one allows to investigate whatever the Event Related Potentials (ERPs) morphological variations can be used to predict the users’ mental states (e.g. attentional resources, mental workload) during different reactive BCI tasks (e.g. P300-based BCIs), and if these information can predict the subjects’ performance in performing the tasks. In the second study, a passive BCI system able to online estimate the mental workload of the user by relying on the combination of the EEG and the ECG biosignals has been proposed. The latter study has been performed by simulating an operative scenario, in which the occurrence of errors or lack of performance could have significant consequences. The results showed that the proposed system is able to estimate online the mental workload of the subjects discriminating three different difficulty level of the tasks ensuring a high reliability.

Actigraphic monitoring of physical activity in clinical trials in schizophrenia

Schizophrenia is still associated with poor outcome, which is mainly related to negative symptoms, reduced physical activity and low quality of life. Physical activity can be objectively measured without distress using wrist actigraphy. The activity levels during the wake periods of the day have been informative on psychopathology and antipsychotic medication. Several studies demonstrated prominent negative symptoms to be associated with reduced activity levels with strongest correlations in chronic patients. Particularly, the avolition score is correlated with reduced activity levels. Moreover, activity levels differ between DSM-IV schizophrenia spectrum disorders and subtypes as well as between patients treated with olanzapine or risperidone. The longitudinal course of activity levels during an psychotic episode demonstrates considerable variance between subjects. During a psychotic episode patients with low activity levels at baseline experience an amelioration of negative symptoms. In contrast, patients with high activity levels at baseline have stable low negative syndrome scores. Between psychotic episodes less variance is observed. Actigraphy is easily applied in schizophrenia and allows collecting large amounts of crosssectional or longitudinal data. With larger numbers of subjects in controlled trials, continuous recording of activity would foster the detection of different outcome trajectories, which may prove as useful groups to target interventions. In clinical trials, activity monitoring may supplement and validate measures of the negative syndrome and its avolition factor or serve as an outcome marker for physical activity, which is important for metabolic issues and quality of life.

Swallowing Activity Assessed by Ambulatory Impedance-pH Monitoring Predicts Awake and Asleep Periods at Night

INTRODUCTION: Voluntary muscle activity, including swallowing, decreases during the night. The association between nocturnal awakenings and swallowing activity is under-researched with limited information on the frequency of swallows during awake and asleep periods. AIM: The aim of this study was to assess nocturnal swallowing activity and identify a cut-off predicting awake and asleep periods. METHODS: Patients undergoing impedance-pH monitoring as part of GERD work-up were asked to wear a wrist activity detecting device (Actigraph(®)) at night. Swallowing activity was quantified by analysing impedance changes in the proximal esophagus. Awake and asleep periods were determined using a validated scoring system (Sadeh algorithm). Receiver operating characteristics (ROC) analyses were performed to determine sensitivity, specificity and accuracy of swallowing frequency to identify awake and asleep periods. RESULTS: Data from 76 patients (28 male, 48 female; mean age 56 ± 15 years) were included in the analysis. The ROC analysis found that 0.33 sw/min (i.e. one swallow every 3 min) had the optimal sensitivity (78 %) and specificity (76 %) to differentiate awake from asleep periods. A swallowing frequency of 0.25 sw/min (i.e. one swallow every 4 min) was 93 % sensitive and 57 % specific to identify awake periods. A swallowing frequency of 1 sw/min was 20 % sensitive but 96 % specific in identifying awake periods. Impedance-pH monitoring detects differences in swallowing activity during awake and asleep periods. Swallowing frequency noticed during ambulatory impedance-pH monitoring can predict the state of consciousness during nocturnal periods

Combining Wireless Sensor Networks and Semantic Middleware for an Internet of Things-Based Sportsman/Woman Monitoring Application.

Wireless Sensor Networks (WSNs) are spearheading the efforts taken to build and deploy systems aiming to accomplish the ultimate objectives of the Internet of Things. Due to the sensors WSNs nodes are provided with, and to their ubiquity and pervasive capabilities, these networks become extremely suitable for many applications that so-called conventional cabled or wireless networks are unable to handle. One of these still underdeveloped applications is monitoring physical parameters on a person. This is an especially interesting application regarding their age or activity, for any detected hazardous parameter can be notified not only to the monitored person as a warning, but also to any third party that may be helpful under critical circumstances, such as relatives or healthcare centers. We propose a system built to monitor a sportsman/woman during a workout session or performing a sport-related indoor activity. Sensors have been deployed by means of several nodes acting as the nodes of a WSN, along with a semantic middleware development used for hardware complexity abstraction purposes. The data extracted from the environment, combined with the information obtained from the user, will compose the basis of the services that can be obtained.

Non-invasive user monitoring in daily tasks for cognitive rehabilitation

El uso de técnicas para la monitorización del movimiento humano generalmente permite a los investigadores analizar la cinemática y especialmente las capacidades motoras en aquellas actividades de la vida cotidiana que persiguen un objetivo concreto como pueden ser la preparación de bebidas y comida, e incluso en tareas de aseo. Adicionalmente, la evaluación del movimiento y el comportamiento humanos en el campo de la rehabilitación cognitiva es esencial para profundizar en las dificultades que algunas personas encuentran en la ejecución de actividades diarias después de accidentes cerebro-vasculares. Estas dificultades están principalmente asociadas a la realización de pasos secuenciales y al reconocimiento del uso de herramientas y objetos. La interpretación de los datos sobre la actitud de este tipo de pacientes para reconocer y determinar el nivel de éxito en la ejecución de las acciones, y para ampliar el conocimiento en las enfermedades cerebrales, sus consecuencias y severidad, depende totalmente de los dispositivos usados para la captura de esos datos y de la calidad de los mismos. Más aún, existe una necesidad real de mejorar las técnicas actuales de rehabilitación cognitiva contribuyendo al diseño de sistemas automáticos para crear una especie de terapeuta virtual que asegure una vida más independiente de estos pacientes y reduzca la carga de trabajo de los terapeutas. Con este objetivo, el uso de sensores y dispositivos para obtener datos en tiempo real de la ejecución y estado de la tarea de rehabilitación es esencial para también contribuir al diseño y entrenamiento de futuros algoritmos que pudieran reconocer errores automáticamente para informar al paciente acerca de ellos mediante distintos tipos de pistas como pueden ser imágenes, mensajes auditivos o incluso videos. La tecnología y soluciones existentes en este campo no ofrecen una manera totalmente robusta y efectiva para obtener datos en tiempo real, por un lado, porque pueden influir en el movimiento del propio paciente en caso de las plataformas basadas en el uso de marcadores que necesitan sensores pegados en la piel; y por otro lado, debido a la complejidad o alto coste de implantación lo que hace difícil pensar en la idea de instalar un sistema en el hospital o incluso en la casa del paciente. Esta tesis presenta la investigación realizada en el campo de la monitorización del movimiento de pacientes para proporcionar un paso adelante en términos de detección, seguimiento y reconocimiento del comportamiento de manos, gestos y cara mediante una manera no invasiva la cual puede mejorar la técnicas actuales de rehabilitación cognitiva para la adquisición en tiempo real de datos sobre el comportamiento del paciente y la ejecución de la tarea. Para entender la importancia del marco de esta tesis, inicialmente se presenta un resumen de las principales enfermedades cognitivas y se introducen las consecuencias que tienen en la ejecución de tareas de la vida diaria. Más aún, se investiga sobre las metodologías actuales de rehabilitación cognitiva. Teniendo en cuenta que las manos son la principal parte del cuerpo para la ejecución de tareas manuales de la vida cotidiana, también se resumen las tecnologías existentes para la captura de movimiento de manos. Una de las principales contribuciones de esta tesis está relacionada con el diseño y evaluación de una solución no invasiva para detectar y seguir las manos durante la ejecución de tareas manuales de la vida cotidiana que a su vez involucran la manipulación de objetos. Esta solución la cual no necesita marcadores adicionales y está basada en una cámara de profundidad de bajo coste, es robusta, precisa y fácil de instalar. Otra contribución presentada se centra en el reconocimiento de gestos para detectar el agarre de objetos basado en un sensor infrarrojo de última generación, y también complementado con una cámara de profundidad. Esta nueva técnica, y también no invasiva, sincroniza ambos sensores para seguir objetos específicos además de reconocer eventos concretos relacionados con tareas de aseo. Más aún, se realiza una evaluación preliminar del reconocimiento de expresiones faciales para analizar si es adecuado para el reconocimiento del estado de ánimo durante la tarea. Por su parte, todos los componentes y algoritmos desarrollados son integrados en un prototipo simple para ser usado como plataforma de monitorización. Se realiza una evaluación técnica del funcionamiento de cada dispositivo para analizar si es adecuada para adquirir datos en tiempo real durante la ejecución de tareas cotidianas reales. Finalmente, se estudia la interacción con pacientes reales para obtener información del nivel de usabilidad del prototipo. Dicha información es esencial y útil para considerar una rehabilitación cognitiva basada en la idea de instalación del sistema en la propia casa del paciente al igual que en el hospital correspondiente. ABSTRACT The use of human motion monitoring techniques usually let researchers to analyse kinematics, especially in motor strategies for goal-oriented activities of daily living, such as the preparation of drinks and food, and even grooming tasks. Additionally, the evaluation of human movements and behaviour in the field of cognitive rehabilitation is essential to deep into the difficulties some people find in common activities after stroke. This difficulties are mainly associated with sequence actions and the recognition of tools usage. The interpretation of attitude data of this kind of patients in order to recognize and determine the level of success of the execution of actions, and to broaden the knowledge in brain diseases, consequences and severity, depends totally on the devices used for the capture of that data and the quality of it. Moreover, there is a real need of improving the current cognitive rehabilitation techniques by contributing to the design of automatic systems to create a kind of virtual therapist for the improvement of the independent life of these stroke patients and to reduce the workload of the occupational therapists currently in charge of them. For this purpose, the use of sensors and devices to obtain real time data of the execution and state of the rehabilitation task is essential to also contribute to the design and training of future smart algorithms which may recognise errors to automatically provide multimodal feedback through different types of cues such as still images, auditory messages or even videos. The technology and solutions currently adopted in the field don't offer a totally robust and effective way for obtaining real time data, on the one hand, because they may influence the patient's movement in case of marker-based platforms which need sensors attached to the skin; and on the other hand, because of the complexity or high cost of implementation, which make difficult the idea of installing a system at the hospital or even patient's home. This thesis presents the research done in the field of user monitoring to provide a step forward in terms of detection, tracking and recognition of hand movements, gestures and face via a non-invasive way which could improve current techniques for cognitive rehabilitation for real time data acquisition of patient's behaviour and execution of the task. In order to understand the importance of the scope of the thesis, initially, a summary of the main cognitive diseases that require for rehabilitation and an introduction of the consequences on the execution of daily tasks are presented. Moreover, research is done about the actual methodology to provide cognitive rehabilitation. Considering that the main body members involved in the completion of a handmade daily task are the hands, the current technologies for human hands movements capture are also highlighted. One of the main contributions of this thesis is related to the design and evaluation of a non-invasive approach to detect and track user's hands during the execution of handmade activities of daily living which involve the manipulation of objects. This approach does not need the inclusion of any additional markers. In addition, it is only based on a low-cost depth camera, it is robust, accurate and easy to install. Another contribution presented is focused on the hand gesture recognition for detecting object grasping based on a brand new infrared sensor, and also complemented with a depth camera. This new, and also non-invasive, solution which synchronizes both sensors to track specific tools as well as recognize specific events related to grooming is evaluated. Moreover, a preliminary assessment of the recognition of facial expressions is carried out to analyse if it is adequate for recognizing mood during the execution of task. Meanwhile, all the corresponding hardware and software developed are integrated in a simple prototype with the purpose of being used as a platform for monitoring the execution of the rehabilitation task. Technical evaluation of the performance of each device is carried out in order to analyze its suitability to acquire real time data during the execution of real daily tasks. Finally, a kind of healthcare evaluation is also presented to obtain feedback about the usability of the system proposed paying special attention to the interaction with real users and stroke patients. This feedback is quite useful to consider the idea of a home-based cognitive rehabilitation as well as a possible hospital installation of the prototype.

Human activity recognition by inertial signals obtained from a smartphone

Human Activity Recognition (HAR) is an emerging research field with the aim to identify the actions carried out by a person given a set of observations and the surrounding environment. The wide growth in this research field inside the scientific community is mainly explained by the high number of applications that are arising in the last years. A great part of the most promising applications are related to the healthcare field, where it is possible to track the mobility of patients with motor dysfunction as also the physical activity in patients with cardiovascular risk. Until a few years ago, by using distinct kind of sensors, a patient follow-up was possible. However, far from being a long-term solution and with the smartphone irruption, that monitoring can be achieved in a non-invasive way by using the embedded smartphone’s sensors. For these reasons this Final Degree Project arises with the main target to evaluate new feature extraction techniques in order to carry out an activity and user recognition, and also an activity segmentation. The recognition is done thanks to the inertial signals integration obtained by two widespread sensors in the greater part of smartphones: accelerometer and gyroscope. In particular, six different activities are evaluated walking, walking-upstairs, walking-downstairs, sitting, standing and lying. Furthermore, a segmentation task is carried out taking into account the activities performed by thirty users. This can be done by using Hidden Markov Models and also a set of tools tested satisfactory in speech recognition: HTK (Hidden Markov Model Toolkit).

Monitoring the economic health of American households : average monthly estimates of income, labor force activity, program participation, and health insurance : first quarter 1984 to third quarter 1991 /

Mode of access: Internet.

Monitoring PAHs in the Brisbane River and Moreton Bay, Australia, using semipermeable membrane devices and EROD activity in yellowfin bream, Acanthopagrus australis

Two water quality monitoring strategies designed to sample hydrophobic organic contaminants have been applied and evaluated across an expected concentration gradient in PAHs in the Moreton region. Semipermeable membrane devices (SPMDs) that sequester contaminants via passive diffusion across a membrane were used to evaluate the concentration of PAHs at four and five sites in spring and summer 2001/2002, respectively. In addition, induction of hepatic cytochrome P4501, EROD activity, in yellowfin bream, Acanthopagrus australis, captured in the vicinity of SPMD sampling sites following deployment in summer was used as a biomarker of exposure to PAHs and related chemicals. SPMDs identified a clear and reproducible gradient in PAH contamination with levels increasing from east to west in Moreton Bay and upstream in the Brisbane River. The highest PAH concentrations expressed as B(a)P-toxicity equivalents (TEQs) were found in urban areas, which were also furthest upstream and experienced the least flushing. Cytochrome P4501 induction in A. australis was similar at all sites. The absence of clear trends in EROD activity may be attributable to factors not measured in this study or variable residency time of A. australis in contaminated areas. It is also possible that fish in the Moreton region are displaying enzymatic adaptation, which has been reported previously for fish subjected to chronic exposure to organic contaminants. These potential interferences complicate interpretation of EROD activity from feral biota. It is, therefore, suggested that future monitoring combine the two methods by applying passive sampler extracts to in vitro EROD assays. (C) 2004 Elsevier Ltd. All rights reserved.